Deep learning to map concentrated animal feeding operations
Deep learning to map concentrated animal feeding operations
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DOI：
10.1038/s41893-019-0246-x
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发表时间：
2019-04-01
影响因子：
27.6
通讯作者：
Ho, Daniel E.
中科院分区：
文献类型：
--
作者：
Handan-Nader, Cassandra;Ho, Daniel E.
环境法的执行主要取决于对集约化动物农业设施的许可和监测，在美国被称为“集中动物饲养业务”（CAFO）。美国目前的法律的格局使政府机构、环保团体和公众很难知道这类设施的位置。因此，许多团体根据地图或实地调查进行了人力和资源密集型的清点，以查明设施。在这里，我们展示了将深度卷积神经网络应用于高分辨率卫星图像，提供了一种有效，高精度和低成本的方法来检测CAFO位置。在北卡罗来纳州，该算法能够检测到589个额外的家禽CAFO，表示从通过手动计数检测到的基线增加了15%。我们展示了该方法如何在地理和时间上扩展，并可以告知合规性和监控优先级。
Enforcement of environmental law depends critically on permitting and monitoring intensive animal agricultural facilities, known in the United States as 'concentrated animal feeding operations' (CAFOs). The current legal landscape in the United States has made it difficult for government agencies, environmental groups and the public to know where such facilities are located. Numerous groups have, as a result, conducted manual, resource-intensive enumerations based on maps or ground investigation to identify facilities. Here we show that applying a deep convolutional neural network to high-resolution satellite images offers an effective, highly accurate and lower cost approach to detecting CAFO locations. In North Carolina, the algorithm is able to detect 589 additional poultry CAFOs, representing an increase of 15% from the baseline that was detected through manual enumeration. We show how the approach scales over geography and time, and can inform compliance and monitoring priorities.